Methods, technology, and systems for quickly enhancing the operating and financial performance of energy systems at large facilities, interpreting usual and unusual patterns in energy consumption, identifying, quantifying, and monetizing hidden operating and financial waste, and accurately measuring the results of implemented energy management solutions, in the shortest amount of time with minimal cost and effort

ABSTRACT

Certain examples relate to computer implemented methods and systems including at least three modules whose combined functionality provides for interpretation and optimization of usual and unusual patterns in energy consumption; the identification, quantification, and monetization of hidden operating and financial waste and the accurate measurement of the results of implemented energy management solutions. The first, “Expert Module”, provides information that assists with the accurate interpretation of the information provided in the Operations module. The second, “Costs Module”, allows users to modify consumption and price components and derive the resulting operating costs based on the facility&#39;s applied or available rate structures. The third, “Operations Module”, charts and evaluates the current operating performance of a facility down to 15-minute intervals in comparison to the operating performance of a pre-selected “base year”. Comparisons are synchronized over intervals in a manner that allows users to compare and comment on individual graphs interactively.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims priority from U.S. Provisional Application No.61/477,956, filed Apr. 21, 2011, the entire contents of which are herebyincorporated by reference. This application is related to U.S. Pat. No.6,366,889 B1 to Joseph A. Zaloom, which issued on Apr. 2, 2002, thecontents of which are incorporated herein by reference.

FIELD

The technology relates to automated computer-implemented algorithmicmethods and systems for conserving energy and/or other resources throughthe ongoing analysis, assessment and enhancement of the operating andfinancial performance of energy and water consuming systems at largefacilities and particularly the consumption of electricity atcommercial, governmental, industrial, home, and other facilities.

BACKGROUND AND SUMMARY

Today's contemporary energy environment is characterized by high energyprices, government mandates to enhance energy efficiency, and a strongdrive to transition to renewable energy resources. Combined, thesefactors are putting strong pressure on building owners and managers tomake lowering energy consumption and cutting energy costs a majorpriority. This pressure is further compounded by the ever increasingnumber of energy efficiency regulations and standards such as Congress'Energy Independence and Security Act of 2007 and the federalgovernment's Energy Star rating program.

While a lot of emphasis has been placed on the use of high efficiencyenergy products and the purchase of clean and renewable energyresources, what is often missing from this strategy is wasteelimination; waste that is embedded in the daily operations of mostcommercial, governmental, and industrial facilities; waste that is soprevalent and so widespread—but to which most Americans are oblivious.It begins at the doorstep of our office buildings and apartments; in ourparking lots and garages; and it is prevalent inside our homes, malls,and offices—a waste which is conservatively estimated at 20% to 30% ofour daily use of energy. Non-limiting examples are: Hallway and stairwaylights which stay on at full capacity 100% of the time even when peopleare not present; parking lots in apartment and office buildings whichstay lit like Christmas trees 100% of the time even when traffic isminimal in the wee hours of the morning; office lights, task lights,monitors, and computers which stay on 24 hours a day despite the factthat no one is there to use them after working hours; escalators andmoving walkways which run continuously regardless of whether people areriding them or not; restroom lights at restaurants and retail outletswhich stay on 24-hour a day, when long stretches of time go by withoutanyone using them. These are but a few of the most glaring examples ofwasteful energy use practices currently prevalent in the United States.Most of this waste can be quickly eliminated with the installation ofcheap motion sensors and simple “On/Off” switches.

In fact, eliminating operating waste is the quickest, most economical,and most environmentally friendly way to save energy and cut costs.

In certain examples, it may be desirable to provide waste reductionstrategies at organizations, large and small, that provide tools toidentify, quantify (measure), and monetize operating waste and savingsopportunities; that can explain usual and unusual patterns in energyconsumption; that can help develop and evaluate highly efficient andeffective energy management solutions; and that can accurately measureand report performance results—on a continuous basis. In certaininstances, without the availability of such tools, there may be lessincentive to take action.

In certain instances, an impediment for the implementation of wastereduction strategies is lack of time, knowledge, and resources. Mostlarge office buildings are owned by investment trusts or relativelysmall organizations that do not have an energy management team on staff;these buildings are typically run by building managers who are too busytackling their day-to-day responsibilities of keeping their tenantshappy to take the time or even to have the knowledge to use commerciallyavailable software to analyze, investigate, diagnose, and document thedaily energy operations of their facilities. In fact, when it comes toenergy, the most important issue for most building managers is energybudgeting; insuring that enough funds are available to pay theirbuilding's utility bills in a timely manner. In some instances, systemsand/or methods providing more than simply energy budgeting may bedesirable.

Moreover, much commercially available energy management software todayis geared towards large enterprises that manage a large number offacilities. This kind of software, whether web-based or desktop based,is in some cases a “cookie-cutter/one-size-fits-all” approach to energymanagement; it organizes billing and energy consumption data intoeye-catching charts and graphs. In certain examples, it may be desirableto also provide context, particularly taking into consideration thedifferent types of facilities, such as whether the facility in questionis a major office building, a warehouse, a hospital, a townhouse, or amajor data processing center, and in some instances tailoring theanalytical and/or reporting approach to at least the type of facility.While most enterprise software can flag the possibility of billing,operational, and mechanical problems, they typically leave it to the“energy manager” to analyze, investigate, diagnose, monetize, anddocument the possibility of such problems. Furthermore, despite the factthat several commercial packages also provide for the charting of energyinterval pulse data from sophisticated metering devices—some, inreal-time, in addition to identifying the possibility that an operating,mechanical, or metering problem has occurred, it may be desirable toquantify, monetize, or to explain the usual and unusual patterns thatare identified.

A variety of systems and services currently on the market providesophisticated energy tracking and monitoring of energy data. However, itmay be desirable to provide quick identification, quantification, andmonetization of operating waste and savings opportunities. In someinstances, it may be desirable to explain the usual and unusual patternsin energy consumption identified by certain systems; as well as quicklyidentify effective and/or efficient energy management solutions, and italso may be desirable to provide accurate measurements of implementedsolutions.

In order to better understand the major shortcomings of existing systemsand methods in optimizing energy operations and eliminating operatingwaste at large facilities one can compare them to trying to help anoverweight person reduce his weight; the best that existing systems andmethods have to offer involves providing facility operations engineerswith systems that can measure and plot energy consumption at theirfacilities in real-time—down to a 15-minute or 30-minute intervallevel—and not much else. Such systems are not known to include aknowledge base or an “Expert System” that can inform facility operatorsof their tenants' operations, the nature of the equipment they use andthe list, power rating, and structure of their facility's mechanical andelectronic systems. They also generally do not include any reference tothe facility's applied utility rates structures nor provide anyindication of the value—e.g., the cost of the energy—being saved at thefacility.

Comparing that method to trying to help an overweight person lose weightis like providing that person with a device that can count his or hercaloric intake and not much else! The age of that person, his or herphysical health; profession (whether the person is a baggage handler ora switchboard operator) do not get taken into consideration. Progress ismeasured at the end of each month when the weight of the person ismeasured against his weight at the end of the prior month. There is nomeasurement of the value of losing weight such as lower cholesterollevel, lower blood pressure, increased stamina, and lower food costs.

Understandably one cannot expect meaningful results from a meremeasurement of real-time energy consumption without context just as onecannot expect a person to undergo a major weight loss from the merecounting of caloric intake.

Elements missing from the above approach, in certain instances, mayinclude:

(1) Lack of a Frame of Reference: Just providing a measurement ofanything is meaningless without a frame of reference. For example juststating that someone is 200 lbs. is meaningless in the context of weightloss. If the person's prior weight was 240 lbs. then that person hasreduced his/her weight. On the other hand, if that person's weight was180 lbs. then that person has gained weight. Therefore, the mereproviding of real-time energy consumption without a comparison to aprior benchmark is of little use.

(2) Content without Context: Only stating that someone is 200 lbs. initself is practically meaningless. It does even not tell if a person isheavy or slim. 200 lbs. on a 5′4″ frame would make a person very heavy.On the other hand, 200 lbs. on a large-framed 6′4″ person is likely toproject an image of fitness and health, and a linebacker who weighedonly 200 lbs. would be considered underweight. Therefore, merelyproviding real-time energy consumption without a context to what type offacility is being tracked (hospital, theater, or office building), thetype of tenants it has, and the type of equipment it contains, ispractically meaningless.

(3) Missing Valuation: The underlying principle of modern economicsociety is money. Money is representative of value. People work formoney, they start businesses to make money, and they sacrifice to savemoney. Therefore, only providing a tool that helps reduce the use of aparticular commodity (i.e. energy) without knowing the quantity that wasreduced and being able to measure the value of the reduced quantity ispractically meaningless.

Below is an analysis of certain offerings of major energy managementcompanies in the United States. None of these companies provide acomprehensive system or method that is capable of quickly interpretingusual and unusual patterns in energy consumption; identifying,quantifying, and monetizing hidden operating and financial waste; andaccurately measuring performance results.

For example, Energy Lens (www.energylens.com) created an Excel add-inthat makes it easy to analyze detailed interval energy data. It allowsusers to study patterns in energy usage, and look for changes in energyperformance. While useful for its intended purposes, Energy Lens' systemmerely flags the possibility that a problem may exist in the dailyoperations of a given facility. In certain instances, Energy Lens may bea basic analytical tool that identifies possible operating andmechanical errors. It may be desirable for a system to allow for thecorrelation of that data with the weather and/or provide for thesynchronization of the operations data from one year to the next toquantify, and also measure differences in operations from one year tothe next. It may also be desirable to provide an Expert system thatcould shed light into the nature of the facility's operations, or on thepossible nature of any displayed irregular consumption data. Andfinally, it may be desirable to provide a cost modeling feature thatcould monetize the effect of the discovered irregular operations onoperating costs.

LPB Energy Management(http://um-online.lpbenergy.com/umo_mansfield/Login.aspx) is an energymanagement company that is currently being acquired by Ecova. LPBprovides a web-based program that generates reports and graphsdisplaying usage and cost statistics, and identifies potential billingerrors and usage anomalies, and compares facilities in order to targetthose with the greatest savings potential. This system provides morecapabilities than Energy Lens' system. This system would be a good matchfor managing multiple small facilities that operate in a similar fashionsuch as 7-Eleven, Macy's, and Best Buy stores, or organizations with amultitude of small facilities. However, it would be desirable, given thenatural variation in energy bills, to provide operations benchmarkingand year over year comparison, and to explain any variation in use,price, and cost; in order to help facility owners, and managers achievegreater energy efficiency. In summary, it would be desirable to provideutilities tracking software with the capability to identify, quantify,or monetize operating waste and savings opportunities.

Ecova (www.ecova.com) lists “Facility Optimization and Efficiency” asone of its solution areas. While Ecova's system does leverage the use ofsmart meters and building automation systems to generate real-timeinformation streams that help identify “poor performers and outliers,”and automatically alert users to conditions outside of pre-definedranges, its system's mainly identifies possible operating and mechanicalerrors in real-time mode. It may be desirable to incorporate an Expertsystem that provides information that helps with the accurateinterpretation of the energy information, cost modeling capabilities,and capabilities for the quick identification, quantification, andmonetization of operating waste and savings opportunities.

First Fuel (http://firstfuel.com) has a Rapid Building Assessmentplatform which uses building consumption data from the utility companyin order to “see” into buildings and understand how energy is being usedthrough end-use benchmarking and then provide actionablerecommendations. However, in some instances, it may be desirable toprovide for synchronized comparative visual analysis with a designatedbaseline in order to measure or quantify performance, and/or to allowusers the ability to comment on the provided information interactivelyin order to share and exchange ideas on the nature of discoveredproblems and opportunities. It may also be desirable to take intoaccount financial information that may enable useful “recommendations”to be made. It may also be desirable to provide methods or systems thatinclude an “Expert” system to enable users to quickly interpret thevisual information unless they are intimately knowledgeable with thefacility's operations.

Tangible Software Inc. (www.tangiblesoftware.com) provides an energysoftware system that addresses six areas of an enterprise's energyinfrastructure: invoice management; acquisition and contract management;building consumption and control; meter data management; demandmanagement; and carbon footprint management. While Tangible SoftwareInc.'s system does provide real-time interval data capture, it may bedesirable to provide systems or methods that provide the ability togenerate synchronized comparative visual analyses of the informationwith a designated baseline, an Expert system for the interpretation ofthe information, and/or a cost modeling capability to monetize possibleproblems and opportunities.

iEnergyIQ (http://ienergyiq.com) is an Enterprise Energy Managementcompany that provides software as a service and energy analytics forenterprises. These services include the ability to generate customizedreports and graphs, as well as identify potential billing errors andusage anomalies. It is also capable of analyzing pulse data from metersand sub-meters. However, while iEnergyIQ is capable of comparing onemeter's stream of pulse data with another account's steam of pulse data,it may be desirable to provide systems and/or methods providingsynchronized comparative visual analyses of the information with adesignated baseline, an Expert system for the interpretation of theinformation, and/or financial modeling capabilities.

eSightEnergy (www.esightenergy.com) is primarily an energy managementsystem for enterprises. It offers a robust suite of web-based onlinemodules with various levels of functionality as well as a desktopcentric version. Basically, this system allows users to track andintegrate energy consumption from meters located in disparate locationsand generate consolidated reports. It is similar to the iEnergyIQ modeldescribed above. It may be desirable to provide systems and/or methods,however, that provide interpretive analyses, quantification andmonetization of identified usage anomalies.

EPS (www.epsway.com) bills itself as a leader in energy managementsolutions for industrial manufacturers. Its approach includes performingan energy audit on the subject facility, making recommendation onreplacing inefficient assets and shifting to alternate sources ofenergy. This company's energy efficiency solutions are based on usingits xChange Point solution. This company's offering is more of anapproach than a system—and it geared towards industrial facilities withlimited application in the commercial office building market. Theapproach includes installing meters to monitor energy usage of systemsand processes at an industrial facility, installing web-based softwareto enable access to the systems “near real-time” data, and have thecompany's team of energy experts make monthly recommendations on how toimprove energy efficiency. However, it may be desirable to furtherinclude an “Expert” module and/or or a financial modeling part withsystems such as this.

Other web-based energy and utility management systems are known such asEnergyCap (www.energycap.com), Abraxas Energy Consulting(www.abraxasenergy.com), Adapt Engineering (www.adaptengr.com), Amersco,(www.ameresco.com), Dynamic Energy Concepts(www.dynamicenergyconcepts.com), Energy-Accounting.com(www.energy-accounting.com), Enernoc (www.enernoc.com), Honeywell(www.honeywell.com), Pace Global (www.paceglobal.com), Pure EnergyManagement (www.pureenergymgmt.com), Utility Management Services(www.utilmanagement.com), EnergyWatchDog (www.energywatchdog.com).

All of the companies listed above can provide systems that can track andmonitor billing and operations data and identify possible billing andoperating anomalies at various levels. However, it may be desirable toprovide systems and methods for an integrated solution that may enableusers to quickly interpret and explain unusual patterns in energyconsumption, that can quickly identify, quantify and monetize operatingwaste and savings opportunities; or that can quickly and accuratelymeasure performance results—on a continuous basis, in certain exampleembodiments. Therefore, a need exists for methods and systems that arecapable of quickly interpreting usual and unusual patterns in energyconsumption; identifying, quantifying, and monetizing hidden operatingand financial waste; and accurately measuring performance results—in areduced amount of time, with reduced cost and effort.

To meet such challenges, for example, certain example embodiments hereinprovide an Expert system to identify, understand and explain usual andunusual operating profiles and/or the ability to measure and quantifyenergy consumption changes (increases/decreases) from the prior year bysynchronizing back exactly (in preferred example implementations) 364days (or multiples of that number, i.e., 728, 1092, 1456 for going backto a base year up to 4 years in arrears). Synchronizing with 363 or 365days will result in Mondays being synchronized with Sundays and/orFridays being synchronized with Saturdays. Also, synchronizing onemonth, three months, or six months in arrears will be less meaningful,as it could straddle seasons where operations are drastically different.This leave the 364 days (exactly 52 weeks) as an example meaningfulsynchronization method, in certain example embodiments. In otherexamples, 350, 357, 371, and/or 378 days may also be close enough so asto result in meaningful synchronization. Correlation can be displayed atthe level of one day, one week, one month, or one year, and anything inbetween, according to different example embodiments.

Furthermore, by adding a financial “What If” module, one can monetizethe quantity of energy that is varying from one year to the next.

Certain example systems and methods disclosed herein may be implementedin different ways. For example, in certain instances, a system may beapplied/method implemented may include performing synchronization“retroactively” once the data has been made available by the utilitycompany. As another example, in other instances, a “Real-Time” systemmay be applied/method may be implemented wherein the comparison weremade between a prior baseline and the current data in real-time. Thissecond approach may be as effective as the first approach in certainexamples, and may trigger automatic action based on pre-determinedcriteria in some instances.

Furthermore, since both consumption and weather data will be collectedconcurrently by the system in certain example embodiments, the systemmay be able to perform regression analysis to determine the correlationand sensitivity of the changes in energy data to the changes in weatherdata. The change of correlation or sensitivity to the weather from oneyear to the next can be a useful indicator or a trigger for automatedaction in “Real-Time” systems.

Additional non-limiting example features and advantages include:

-   -   The synchronization of current or recent operating patterns with        the corresponding operating patterns of the prior year—or any        other pre-selected based year.    -   A synchronization that is based on a 364 days difference from        one year to prior year as well as any multiple of that        number (364) for each additional year in excess of one year.    -   The synchronization can start by any day of the week—not        necessarily by Monday. For example, the weekly synchronization        can start on a Sunday, or a Friday, or any other day.    -   A synchronization that can range from a comparison of hour to        another, one day to another, up to one year and another—and        anything in between.    -   A synchronization that allows for the identification of usual        and unusual energy consumption patterns.    -   A synchronization than allows for the accurate measurement and        quantification of the difference in energy consumption resulting        from the difference in the displayed operating patterns.    -   The addition of an interactive commenting and discussion log        that can reference, document, and explain the usual and unusual        operating patterns displayed in the Operations Module.    -   The addition of tabular consumption, temperature, and cost data        at the bottom the operating profiles of real-time data streams.    -   The combination of an “Expert” Module that would help in the        interpretation and explanation of displayed operating patterns        and leverages information collected from system participants to        optimize the efficiency of commodity markets, collaborate on        finding solutions to common problems, as well as discovering new        technologies and the sharing of knowledge and ideas regarding        the availability and the implementation of new energy systems,        methods, and technologies.    -   The method of the composition of the “Expert” Module.    -   The combination of a “Costs” module that could monetize the        difference in operating patterns from one year to the next as        well as from one year to a preselected base year.    -   The use of the above methods separately or in combination with        each other.    -   Triggering automated notifications and visual and audible        warning signals whenever certain combinations of operating        conditions and temperature differences (from the prior year or a        preselected base year) have been met.    -   Triggering predetermined Action Control Scripts based whenever        certain combinations of operating conditions and temperature        differences (from the prior year or a preselected base year)        have been met.    -   Tracking is not limited to energy and weather on the chart, it        can include other parameters of interest to the user, such as        hotel vacancy rates, number of meals served, etc.

In certain example embodiments, a method of synchronizing current and/orrecent operating pattern(s) with corresponding operating pattern(s) of aprior year may be provided. The method may comprise storing currentand/or recent operating patterns on at least one storage device, thecurrent and/or recent operating patterns comprising current and/orrecent incremental commodity usage data. The method may further comprisestoring operating patterns of at least a year prior to the currentand/or recent operating patterns on the at least one storage device, theprior year operating patterns comprising historical incrementalcommodity usage data. The current and/or recent operating patterns maybe synchronized, via a processor coupled to the storage device, withoperating patterns of at least a year prior, based at least in part onperiodically-repeating time periods over which the usage occurred togenerate an incremental historical comparison of usage from differentbut related periodically-repeating time, that at least partly takes intoaccount time-variable factors affecting the usage, including using theprocessor to automatically correlate usage data that is exactly aninteger multiple of 364 days apart, in certain example embodiments.

Other example embodiments may relate to a system for monitoring andreporting usage of a commodity such as energy. The system may include atleast one storage device configured to store historical incrementalcommodity usage data. The system may further include a processor coupledto the storage device, the processor time-correlating the historicalincremental usage data based at least in part on periodically-repeatingtime periods over which the usage occurred to generate an incrementalhistorical comparison of usage from different but relatedperiodically-repeating time periods that at least partly takes intoaccount time-variable factors affecting the usage. Furthermore, incertain instances, the processor may automatically identify unusualusage patterns by comparing with synchronized historic usage patternsand automatically providing contextual interactivity for the usagepatterns based on a stored knowledge base, temperature- and/orconsumption-based action control lists, and information includingcommodity pricing, and/or facilities analytics.

In further example embodiments, a system for tracking performance of afacility relating to energy and/or water usage may be provided. Incertain instances, the system may include at least one storage deviceconfigured to store historical and/or current incremental commodityusage and/or cost data. The system may further comprise a firstcomputer-implemented module comprising a plurality of components, thecomponents comprising a facility information component, a cost analysiscomponent, and an operations analysis component. In certain cases, thefacility information component may comprise data pertaining to aparticular facility, comprising a description of the facility, afunction of the facility, operating hours of the facility, and/orinformation relating to usage of utilities by the facility, stored onthe storage device. The cost analysis component comprising historicalbilling data may be stored on the storage device. The operationsanalysis component may comprise data corresponding to a plurality ofweekly modules comprising 15 to 30 minute interval usage and weatherdata for each day of the week. Additionally, a secondcomputer-implemented module may comprise periodic updates to the costanalysis and operations analysis components. The system may also includea processor configured to execute the first and secondcomputer-implemented modules. Furthermore, the data corresponding to theweekly modules from a prior year and from a current year may besynchronized by day of week such that data from a Monday of a particularweek in the prior year and data from a Monday of a corresponding week inthe current year are synchronized in order to accurately trackperformance of the facility with respect to energy and/or water usage.In certain example embodiments, this synchronization may result in datafrom a given day being correlated with data from a day 364 days prior tothe given day.

In other example embodiments, the modules may be daily rather thanweekly. Further, rather than synchronizing by day of the week, the datacorresponding to the modules from a prior year and from a current yearare synchronized by day of week such that data from the given day anddata from a day 364 days before the given day and/or a multiple thereofare synchronized in order to accurately track performance of thefacility with respect to energy and/or water usage.

Further example embodiments relate to a method of tracking performancetrends relating to utility usage. The method may comprise synchronizing15 to 30 minute intervals of data from a particular week in first yearwith 15 to 30 minute intervals of data from a corresponding week in asecond year such that the data is synchronized by day of the week inorder to track the performance of utility usage from the particular weekof the first year to the corresponding week of the second year bycomparing usage from the week of the first year and the week of thesecond year in 15 to 30 minute intervals.

Still further example embodiments relate to a non-transitory storagemedium arrangement for in use being operatively coupled to a computingdevice. The computing device in use may access the storage mediumarrangement to generate an output including at least user display data.The storage medium arrangement may store at least instructionsexecutable by computing device to correlate data stored on the storagemedium arrangement and generate said user display data output. Incertain examples, the storage medium arrangement may have storedthereon: utility usage data defining at least utility usage including anamount of usage and time periods corresponding to said usage; andinstructions executable by said computing device to process usage amountat least in part based on said time periods to correlate and registersaid usage data accordingly to utility demand patterns that are likelyto recur in time, to thereby enable comparison between time-comparabledemand cycle patterns.

In certain example embodiments, the correlation may be performed byaligning a given day in a current year with the day that is exactly 364days before the given day. In other example embodiments, the correlationand/or synchronization may be with a day that is a multiple of 364 daysprior to the given day, and/or with a day that is close to 364 daysprior to the given day and is divisible by 7 (e.g., 350, 357, 371, 378,and the like).

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1( a)-(b) illustrate an example of a retroactive analysis systemfor improving operating and/or financial performance of energy;

FIG. 2 illustrates an overview of example software architecture of theFIG. 1( a) system for improving operating and/or financial performanceof energy, according to certain example embodiments;

FIG. 3 illustrates an example software processing of the FIG. 1( a)system for identifying, quantifying, and monetizing differences inenergy use from the same period of a prior year;

FIG. 4 illustrates a more detailed process workflow diagram related toexample systems and/or methods;

FIG. 5 shows an example comparison of one day in a baseline year to thecorresponding day in a current year;

FIG. 6 shows an example comparison of one week in a baseline year to thecorresponding week in a current year;

FIG. 7 illustrates a workflow process according to certain exampleembodiments;

FIG. 8 is a flow diagram illustrating certain example components of anexample Expert module;

FIG. 9 illustrates an example overview of the Expert module;

FIG. 10 is a block diagram illustrating certain example embodiments ofthe Expert module;

FIG. 11 illustrates a snapshot of an example (e.g., partial) Expertmodule;

FIGS. 12( a)-(b) illustrate example methods relating to line-item byline-item recompilation of a facility's monthly electric bills;

FIGS. 13( a)-(b) illustrate snapshot of example “What If” financialmodeling according to certain example embodiments;

FIGS. 14( a)-(b) illustrates partial snapshots showing how “What If”financial modeling may allow a user to modify any aspect of energy useand/or price according to certain aspects;

FIGS. 15( a)-(g) illustrate example screen shots and graphs of a VisualIndicators component;

FIGS. 16( a)-(d) illustrate certain example embodiments of a cost moduleand example interface(s) for online access of the cost module;

FIG. 17 illustrates a facilities operations analysis according tocertain example embodiments;

FIGS. 18( a)-(e) illustrate an example method of analyzing energyoperations over the period of a week according to certain aspects;

FIGS. 19( a)-(c) shows example interface(s) relating to online access tothe Operations Module;

FIG. 20 illustrates an example user interface displaying and/orpermitting interactive comment;

FIGS. 21( a)-(c) illustrates an example embodiment of a visual andtabular display of daily energy and weather data (e.g., snapshots froman example Weekly Module);

FIGS. 22( a)-(b) illustrate another example aspect of the functionalityof the Weekly Module;

FIGS. 23( a)-(b) illustrate screen shots from an example “delayed” datasystem/retroactive analysis, and an example “real-time” system,respectively, according to certain example embodiments;

FIG. 24 illustrates an example picture of “real-time” or “live” datastream systems;

FIG. 25 illustrates that certain example live data stream embodimentsmay include an Expert module, a Costs module, an Operations module, andAction control lists;

FIGS. 26( a)-(c) illustrate a “Costs” template and architectureaccording to certain example embodiments and example schematics ofpossible Costs Module “What-If” Template;

FIG. 27 illustrates how synchronizing to a period exactly 364 days inarrears in a given year in a non-limiting exemplary example embodiment,will result in a match for the same weekday from one year to another;and

FIG. 28 is a graph illustrating example results of daily synchronizationwith a prior year in certain example embodiments.

DETAILED DESCRIPTION

Certain example embodiments relate to a computer implemented method andsystem which may include three modules whose combined functionalityprovides for the quick enhancement of the operating and financialperformance of energy systems at large facilities; the interpretation ofusual and unusual patterns in energy consumption; the identification,quantification, and monetization of hidden operating and financialwaste; and the accurate measurement of the results of implemented energymanagement solutions.

In certain example embodiments, the methods and systems relating tosynchronization described herein may operate in a “retroactive analysis”fashion. In other words, the identification, quantification, andmonetization of hidden operating and financial waste (e.g., in terms ofutility usage) may be analyzed “after the fact”, in certain cases. Inother example embodiments, a “real time” analysis may be performedinstead of or in addition to the retroactive analysis. Exampleembodiments of the delayed/retroactive analysis will be described first.

FIG. 1( a) illustrates an example overview of certain exampleembodiments of a “retroactive analysis” system for improving operatingand/or financial performance of energy as described herein. Certainexample systems and methods relating to the “retroactive analysis” mayinclude at least three modules: an expert module, a costs module, and anoperations module.

In FIG. 1( a), resource delivery 10 (which can be one or a number ofentities or distribution channels) delivers resources such as energy,water, and/or any other commodity to a facility or facilities 14(1),14(2) . . . 14(n). In certain instances, one facility may be provided;however, in non-limiting example implementations, one or more resourcedelivery agents may deliver resources to a plurality of facilitiesincluding a plurality of meters, as well as a plurality of facilitymanagement entities and/or end users. Each facility may comprise one orseveral buildings, rooms, or other physical entities capable of using orconsuming the delivered resource.

The amount of resources delivered is characteristically measured bymeters 12(1), 12(2) . . . 12(n), or another measuring device.Information relating to the usage of these resources is sent to (and/orretrieved by) a utility company load management database 16. Via anetwork 18 or otherwise, this information may be retrieved by a computer20.

Computer 20 includes a processor 24, which may be connected tonon-transitory storage medium 32, user interface 28, input device 26,and display device 30. Data 34 is retrieved from database 16 and enteredinto computer 20. The computer 20 also includes an Expert module 100, aCost module 200, and an Operations module 300, in certain exampleembodiments. The data 34 is processed (e.g., by processor 24, inconnection with at least one of the modules) to produce results/graphs36. Results/graphs 36 are sent to distributer facility managementserver(s) 40(1), 40(2) . . . to 40(m) via network(s) 38.

A facility management server 40(1) may include a processor 42(1),non-transitory storage device 46(1), and network communications 44(1).The information (e.g., results/graphs 36) stored in server 40(1)/storagedevice 46(1) are accessible via user interface 48(1) (e.g., a webpage,file, program, etc.), and may be accessed by an end user 52(1) (e.g., anemployee, the client, the management office, etc.). The information(e.g., results/graphs 36) stored in server 40(1)/storage device 46(1)may also be sent as an email report 50(1) to the end user 52(1) (e.g.,the client, management office, etc.).

Similarly, server 40(2) may include processor 42(2), non-transitorystorage device 46(2), and/or network communications 44(2). Theinformation (e.g., results/graphs 36) stored in server 40(2) /storagedevice 46(2) are accessible via user interface 48(2) (e.g., a webpage,file, program, etc.), and may be accessed by an end user 52(2) (e.g., anemployee, the client, the management office, etc.). The information(e.g., results/graphs 36) stored in server 40(2)/storage device 46(2)may also be sent as an email report 50(2) to the end user 52(2) (e.g.,the client, management office, etc.). Server 40(m) may include processor42(m), non-transitory storage device 46(m), and/or networkcommunications 44(m). The information (e.g., results/graphs 36) storedin server 40(m)/storage device 46(m) are accessible via user interface48(m) (e.g., a webpage, file, program, etc.), and may be accessed by anend user 52(m) (e.g., an employee, the client, the management office,etc.). The information (e.g., results/graphs 36) stored in server40(m)/storage device 46(m) may also be sent as an email report 50(m) tothe end user 52(m) (e.g., the client, management office, etc.).

In other words, certain example systems and methods may include aplurality of facilities (e.g., up to “n”), and computer 20 may processcommodity usage data from the plurality of facilities 14, and may sendthe customized customer-specific processed information to a plurality offacilities management(s) and/or end users (e.g., up to “m”). Thus, thenumber of facilities and facility management may correspond one-to-one,in certain example instances; however, in some cases these may not socorrespond (e.g., when a facility includes more than one location and/orbuilding, when a facility is managed by more than one facilitymanagement unit, or when one facility management unit manages more thanone facility, etc.).

FIG. 1( b) illustrate a non-limiting example a system according tocertain example embodiments. FIG. 1( b) shows a general overview ofinformation relating to energy/resource use in facilities being sent toa utility company load management database. A computer is used by a userto log into the utility company's database and retrieve data, which ismanually entered into the computer (e.g., via an input device—but may beautomatically sent in certain examples). Data may be placed in an Excelspreadsheet in certain example embodiments. The data is then processed,e.g., utilizing the Expert module, Cost module, and/or Operationsmodule, to create related graphs and/or tables. These graphs and/ortables are then stored on an internal company server. The informationfrom the data and graphs and/or tables may be sent to and/or retrievedvia a user interface such as a webpage. In other examples, theinformation may be stored in a file and/or program. Then, theinformation including the graphs and/or tables (and possibly the data)is sent to an end user such as a management office.

FIG. 2 illustrates an example overview of example systems and/or methodsfor improving the operating and financial performance of energy systemsat larger facilities; interpreting usual and unusual patterns in energyconsumption; identifying quantifying, and monetizing hidden operatingand financial waste; and accurately measuring the results of implementedenergy management solutions, in a reduced amount of time, with reducedeffort and/or cost.

In certain instances, an example system may include at least an Expertmodule, a Cost module, and an Operations module.

The first, “Expert module”, provides information that may assist withaccurate interpretation and improvement (e.g., optimization) of theinformation provided in the Operations module.

The second, “Costs module”, allows users to modify consumption and pricecomponents and derive resulting operating costs based on the facility'sapplied rate structures.

The third, “Operations module”, charts and evaluates the currentoperating performance of a facility down to 15-minute intervals incomparison to the operating performance of a pre-selected “baseyear”—identifying and quantifying hidden operating and financial wasteas well as providing accurate measurements of the change in operatingperformance from one year to the next. Comparisons are synchronized overweekly intervals in a manner allowing users to comment on each graphinteractively.

Certain example synchronization systems and methods described herein mayinclude an Expert module, a Cost module, and an Operations module, thatsymbiotically work together in order to improve the operating andfinancial performance of energy systems at large facilities. Forexample, certain example systems and methods may relate to interpretingusual and unusual patterns in energy consumption; identifying,quantifying, and monetizing hidden operating and financial waste; andsubstantially accurately measuring the results of implemented energymanagement solutions. In certain instances, example methods and systemsdisclosed herein may advantageously provide the above-described analysisin a reduced amount of time, with reduced cost and/or effort.

Further example embodiments relate to a system for processing historicalutility usage and cost data collected from a utility, said systemincluding: at least one storage medium that stores said utility usagedata; at least one user input/output device; at least one processoroperatively connected to the storage medium and the user input/outputdevice, the at least one processor automatically creating a baselinebased on at least said stored utility usage and cost data, said at leastone processor further updating the stored utility usage and cost datawith more current usage and cost data and synchronizing the updated datawith previously stored data to generate graphical visualizations thatallow discovery of performance discrepancies and/or usage or costaberrations compared to said baseline; and a display device operativelycoupled to the at least one processor, the display device displayingsaid generated graphical visualizations.

In other example embodiments, the at least one processor further may actthrough the input/output device and may enable a user to interactivelylog causes for said discrepancies and/or aberrations. In other examples,the at least one processor may further act through the input/out device,which may enable a user to use an interactive log in order to search forsolutions to similar discrepancies and/or aberrations.

FIG. 3 illustrates an example system and/or method for identifying,quantifying, and monetizing differences in energy use from the sameperiod of a prior year. The steps in FIG. 3 include: (S1) identifying,quantifying, and monetizing differences in energy use from the sameperiod of a prior (e.g., the prior) year; (S2) identifying unusual orhigh/low energy consumption patterns in comparison to the prior year;(S3) quantifying the difference in energy use by replacing in computermemory the stored data representing unusual patterns or high increasesor decreases in energy use with data representing normalized or historicpatters of energy use (taken from historical or the prior year'srecords); (S4) monetizing the difference in cost due to the differencein energy use by reprocessing the stored billing data using thenormalized patterns (or historical energy levels) to determine theenergy cost under the normalized or historical conditions; (S5)documenting and reporting the differential in cost of the unusual(and/or high/low) patterns of energy consumption over thenormalized/historical patterns of energy use and consumption.

FIG. 4 illustrates a more detailed process workflow diagram related tothe overview of example systems and/or methods described with respect toFIG. 3. These processes are described in more detail below.

Certain example Operations modules may include a data input step. Thisis shown as S1 in FIG. 4. With respect to data input, the energyconsumption intervals may be downloaded and/or retrieved from a database(e.g., a database owned and/or operated by a utility or commoditycompany). In certain instances, the consumption intervals may be in 15,30, or 60 min. increments. However, in other examples, the consumptionintervals may be provided in any appropriate increment. Furthermore,weather data may also be downloaded and/or retrieved.

Additionally, in certain example Operations modules, the sameconsumption intervals for a previous year may also be downloaded and/orretrieved in order to create a baseline to which the more current energyconsumption/cost may be compared. In certain example embodiments, thebaseline energy use and weather data may be acquired by going back intime exactly 364 days from the target date for one year in arrears (orany reasonable multiple of 364, e.g., 364*X, where X is anywhere from 1to 5, etc.). In certain instances, X may represent the number of yearsin arrears. It will be appreciated that in certain instances, it mayalso be relevant to go back 350, 357, 371, and/or 378 days (e.g., to thesame day of the week in the same (e.g., corresponding) week of aprevious year, or the same day of the week in a week close to thecorresponding week of a previous year).

The Operations module may further include data processing by a computerin certain example embodiments. This is shown as S2 in FIG. 4. Thegraphical representation of the daily energy consumption and weathertemperatures may be synchronized by comparing the current year's data toa prior/baseline year's data, for the period in question, in certainexamples.

In S3, an Expert module may be used to perform an information analysis,in certain example embodiments. For example, the information from anexample Expert module may be used to identify and interpret inefficientoperations, in certain example embodiments. Further, the areas ofpotential reduction may be measured and quantified by comparingdifferences in energy use, between the current year and the base year,in some instances.

From S3, one may proceed directly to S7, or one may continue with stepsS4-S6 prior to arriving at S7. In certain examples, S7 may be performedtwice—after S3 and again after S6. In certain example embodiments, afterany areas of potential energy reduction are identified, interpreted,measured, and quantified, these results may be reported by documentingthe identified, quantified, and monetized savings opportunities ininteractive logs. In further examples, email notifications may be sentto relevant personnel.

S4-S6 are related to the cost module, and in certain examples, the costanalysis may also include data input (S4), data processing by a computer(S5), and information analysis (S6).

In certain example embodiments, the data input step of the cost analysismay include downloading and entering utility bills into the Cost module.More specifically, the data input step (S4) may include (a) downloading,scanning, or otherwise introducing a utility bill into a system; and (b)inputting the entries in the utility bill into the corresponding costanalysis module, according to certain example embodiments.

This may be followed by data processing by a computer (S5) in certainexample embodiments. The data processing by computer (S5) may includeline-item by line-item recompilation of month bills based on the appliedrate structures. In certain examples, this may include detailed cashflow analyses.

More specifically, a computer may generate an output including, forexample: (a) detailed line-item by line-item recompilation of themonthly utility bills included in the system—including a detailedrepresentation of the monthly cash flow transactions in certainexamples; (b) graphical and/or tabular representations of certainperformance indicators of utilities (e.g., energy and/or water) use,price, and/or cost on a monthly, year-to-date, and/or historical basis(these representations may include more or less data than describedherein according to different example embodiments; (c) graphical and/ortabular representations of, for example, daily, weekly, monthly,year-to-date, and/or yearly utilities (e.g., energy and/or water) useper gross square foot as compared to a specific base year and/or arelated energy reduction baseline (for users who wish to compare currentenergy use to a baseline older than the two most recent fiscal years,etc.); (d) posting information (e.g., an image, graph, table, etc.)relating to the line-item by line-item recompilation of the costanalysis of certain utilities accounts (e.g., large electric/gasaccounts, or any other accounts) to the corresponding account page ofthe cost analysis section of the webpage and/or program.

During cost analysis, the processing of data by a computer may befollowed by an information analysis (S6), in certain exampleembodiments. The information analysis step of cost analysis may includechecking the Operations module to insure that the reported increaseand/or decrease in the billing data is corroborated by the correspondingconsumption and weather data, if, for example, billing indicators showout-of-range parameters. Further, the information analysis step (S6) mayinclude consultation with the Expert module for any renovation, specialevent, shut-down of facility, e.g., or the like, related to anyout-of-range parameters. Furthermore, in (S6) any computation and/orcash flow discrepancies, as well as any quantified areas of potentialenergy reduction (e.g., as identified by the Operations module), may bemonetized, for example, by recompiling billing costs based onalternative energy consumption data, in certain example embodiments.

More specifically, in certain cases, an example information analysis mayinclude reviewing the line-item by line-item recompilation process formathematical accuracy. This may include verifying compliance withcontractual billing obligations between client facilities and commodityand/or utility companies. The information analysis may further includereviewing the graphical and/or tabular representation of certainindicators of out-of-range parameters in certain cases. The informationanalysis may also include checking contract records and/or issuestracking databases for additional information related to the account ifbilling discrepancies are encountered (e.g., check for a recent ratechange, a pending billing issue, and/or the like). The operationsanalysis component may also be checked to ensure that any reportedincrease and/or decrease in billing data is corroborated by theoperations and/or weather data if the certain performance indicatorsshow out-of-range parameters.

The information analysis aspect of the cost analysis (e.g., S6) may alsooptionally include documenting reasons for discrepancies (e.g., ifinformation is found to justify and/or explain the discrepancies) forfuture reference. If no reason for a discrepancy is found (e.g., if thediscrepancy remains unexplained), an alert may be sent to a userrequesting clarification in some cases. In certain examples, this alertmay be a “push alert” sent via email. The issue may further be loggedunder the “issues tracking” section/component for optional furtherpursuit and/or analysis if no information is found that can justifyand/or explain a discrepancy.

During cost analysis, the information analysis may be followed by reportgeneration, in certain example embodiments (S7). In certain examples,report generation may include generating an image/file of the line-itemby line-item bill recompilation and/or the graphical and/or tabularrepresentation of certain performance indicators of each utilityaccount. This generated file may be sent to the user (e.g., emailedand/or the like) and/or made available for the user's review (uploadedto a webpage or centralized server, sent to the user for downloading,any method of making a file available to a user, etc.).

A second aspect of a “delayed delivery” synchronization system and/ormethod may involve the process of incrementally updating the operationsanalysis section/component. The processes of updating the operationsanalysis may also include data input, data processing, informationanalysis, and/or report generation in certain example embodiments.

In certain example embodiments, the data input may include tabulatingincremental use and/or weather data for each current period (e.g., day,week, month, quarter, etc. of the current year). For example, FIGS. 5and 6 illustrate how energy profiles from a prior (e.g., baseline) yearand a current year may be synchronized to form one graph that includes arepresentation of the energy profiles. Further, in certain exampleembodiments, the corresponding weather from the baseline year and thecurrent year is added to the graph in order to compare both the energyprofile differences and weather differences between the baseline timeperiod (e.g., hour, day, week, month, etc.) in the baseline year with acurrent time period (e.g., the more current hour, day, week, month,etc., for which performance analysis or the like is sought) in thecurrent year.

FIG. 5 is a graph illustrating a comparison of one day in a baselineyear to the corresponding day in a current year, while FIG. 6 is a graphillustrating a weekly correlation.

In some cases, the usage intervals may be inputted into a file (e.g., aspreadsheet or the like). However, the data may be inputted to anyprogram, webpage and/or any other suitable holder for the data (e.g., anExcel spreadsheet). In certain example embodiments, the intervals ofusage may be 15 minute, 30 minute, 60 minute, and/or any appropriateinterval, for each day (e.g., as illustrated in the graphs of FIGS. 5and 6). In other examples, the usage intervals may be in any incrementnormally provided by a utility company. The usage intervals may also beinputted in any increment desired by a client/customer. The entry and/ordownloading of this data may be performed as the data is made availableby the utility company, or at other pre-determined intervals. Theweather data corresponding to each day and/or usage interval may beinputted and/or downloaded etc. into the file containing the usageintervals, too, in some cases.

The information from the inputted data may be processed by a computer,and the computer may produce a computer-generated output, in certaincases. The computer-generated output may include synchronized graphicalrepresentation of the correlation of the daily energy consumption andweather temperatures comparing the current year's data to a prior year'sdata for any given corresponding day and/or time period as implementedin the data inputting step(s) described above. In certain exampleembodiments, this will advantageously allow for the synchronizedgraphical analysis of the hourly, daily, weekly, etc., operations fromone year to the next; a Monday will always be synchronized with aMonday, and a Sunday will always be synchronized with a Sunday. Forexample, the synchronization may be between any given day and a dayexactly 364 days prior to the day in question, as described above withrespect to certain example embodiments. To do otherwise, such as tryingto synchronize by month or by day of year, may result in a mismatch ofdays and operating profiles, which may in turn make the comparisonuseless. For example, April 4th of 2011 falls on a Monday (a workingday), whereas, April 4th of 2010 falls on a Sunday (a weekend). Theoperating profiles of a work day and a weekend day may be drasticallydifferent, and thus an accurate energy and/or utility-usage comparisonmay not necessarily be made.

The computer-generated output may also include tabular and/or graphicaldisplay of the daily energy and weather data for any given day and/ortime period in comparison with the corresponding period of a prior year,in some cases. In further examples, the output may also include thehourly, daily, weekly, etc., differences between the current year's dataand the prior year's data, which may be expressed as a percentage (e.g.,the various percentages that these differences represent). Thecomputer-generated output may also include tabular and/or graphicalrepresentation of the interval data (e.g., 15-minute, 30-minute,60-minute interval data, etc.) used in generating the synchronizedcorrelation graph described above. An image of each graph (e.g., hourly,daily, weekly, monthly, etc.) may be posted to the corresponding accountpage of the operations analysis component/section of a webpage, fileand/or program, etc., in certain cases. In certain non-limitingimplementations, the graphs may be weekly. In other cases, aninteractive log may be provided under each periodic graph posted in the“Operations” section of the website, file, program, etc., to allow usersto comment on, or explain, the information presented in the graph. Thismay be particularly advantageous if there is an easily-recognizablediscrepancy, such as a large difference in temperature, a conference, aholiday (e.g., July 4^(th)), and/or the like. In cases such as those,the interactive log may be used to describe the reason for thediscrepancy.

Another aspect of the operations analysis component of certain examplesynchronization systems and/or methods is information analysis, incertain example embodiments. In certain example instances, theinformation analysis may include the data may be analyzed by reviewingthe daily graphs such as that shown in FIG. 5 to determine the presenceof possible operating errors, equipment problems, and/or hard-to-detectbilling errors. Other periodic intervals of time may also be used (e.g.,weekly, as shown in FIG. 6, or even monthly and/or yearly, or hourly,according to different example embodiments.) This may be done byidentifying irregular profiles and comparing them to documented patternsof unusual circumstances. These could represent inefficient operationsfor which solutions are known based on historical empirical analysis incertain example embodiments, as described by the methods of U.S. Pat.No. 6,366,889 B1 by the same inventor. The information analysis may alsoinclude identifying areas of potential energy reduction based on adetailed assessment of the current and historical daily operatingprofile taking into consideration the function of the facility, itsweekly operating hours, and/or other relevant parameters. For exampleonly, an office building that uses 60% of its peak operating energyduring nights and weekends, when most of the staff are gone, may havepotential to reduce operating costs through the adoption of vigilantoperations, the installation of more efficient lighting, theinstallation of motion sensors, the installation of a computerizedenergy management system, and/or a combination of any of the above.

The information analysis may further include measuring the results ofchanges in operating performance from the implementation of innovativesolutions by easily identifying, quantifying, and monetizing differencesin energy use from the same period of the prior year. It may alsoinvolve checking the corresponding weather data and/or other relevantcomponents/sections of the expert system for any renovation, expansion,and/or shut-down of the facility if the daily graphs show unusualincreases and/or decreases in energy use.

In certain example embodiments, the reasons for discrepancy (e.g.,unusual increases and/or decreases in energy use), if known and/ordiscovered, may be documented in a log below the graph (e.g., theinteractive log). In other embodiments, an alert may optionally be sentto the user requesting clarification, and optionally logging the issueunder the issues tracking section for further pursuit and/or analysis ifno information is found that can justify or explain the unusualincreases or decreases in energy use.

The report generation aspect of the operations analysis may includegenerating a file and/or image corresponding to each newly generatedgraph, and sending and/or making available the file to the user.

For example, FIG. 7 illustrates an example workflow process for expertmodule 100, cost module 200 and operations module 300. In certainexample embodiments, the cost analysis and/or operations analysismodules may include (1) data input; (2) data processing by computer; (3)information analysis; and (4) report generation. In certain exampleembodiments, if any problems arise during the information analysis ofsaid data, the expert module comprising at least knowledge data base maybe checked for a solution. If no solution is found, an investigation maybe performed. In any event, a report will be generated after dataprocessing and information analysis, in certain example embodiments.

Turning more specifically to the Expert module, certain example Expertmodules/systems described herein may serve as an indispensableinteractive and expandable knowledge base that provides context for aquick understanding of the links between the operating and financialperformance of a given facility and its mechanical systems, itsfunction, operating hours, tenant activities, energy rates, maintenanceissues, occupancy, weather, and any other relevant factors, in certainexample embodiments. The Expert module may serve as the “memory” of anexample system. It may provide instant access and reference to relevantknowledge for the accurate identification and interpretation ofinefficient operating and financial performance related to usual andunusual operating patterns in energy consumption.

In certain exemplary embodiments, the Expert module may serve as anall-encompassing, interactive, fully indispensable and/or expandableknowledge base that collects and/or organizes information from aplurality of system participants. The Expert module may present theinformation in a manner that provides for the interpretation of theinformation presented in the Operations module, and/or theidentification of inefficient operational practices. Furthermore, incertain instances, the Expert module may leverage information collectedfrom the plurality of system participants to improve their purchasingpower with respect to electricity and/or gas commodities. The Expertmodule may also provide a forum for the sharing and exchange ofknowledge and ideas, in certain example embodiments. It may furtherprovide a collection of facts and/or rules for solving problems as wellas providing context for a quick understanding of the links between theoperating and financial performances of a given facility, in certaininstances. The Expert system/module may also provide context for a quickunderstanding of the links between the aforesaid performance of a givenfacility, and its mechanical systems, function, operating hours, tenantactivities, energy rates, maintenance issues, occupancy, special eventsand/or situations, weather, and/or any other relevant factors.

FIG. 8 is a flow diagram showing that certain example components, suchas facility records, may be inputted into the Expert module. Thisinformation may include general information, utilities information,systems information, and/or interactive notes.

FIG. 9 illustrates an example overview of the Expert module. In certaininstances, the Expert module may include at least some and/or all of thefollowing four components: (1) Facility records, (2) Knowledge Base, (3)Action Control Lists, and/or (4) Brain Trust.

Turning more particularly to each of these components, in certainexample embodiments, the Facility Records component may aggregate muchand/or all information related to a given facility under one roof. Itmay provide context for a quick understanding of the links between theoperating and financial performance of a given facility and itsmechanical systems, its function, operating hours, tenant activities,energy rates, maintenance issues, occupancy, weather, and any otherrelevant factors, in certain example embodiments.

It may provide instant access and reference to relevant knowledge forthe accurate identification and interpretation of inefficient operatingand financial performance related to usual and unusual operatingpatterns in energy consumption. The Facility Records component mayinclude General Information, Utilities Information, Systems Information,and Interactive Notes

The General Information section provides basic information regarding thefacility, such as a picture of the facility, its address (also displayedon a live Google map), function, total area, year built, operatinghours, tenants' makeup and operations, the presence of any specialoperations or equipment, such as a data center, labs, auditorium, and/orthe like.

The Utilities Information section provides the name of the utilitycompanies serving the facility, the number of utility accountsassociated with each utility, and the number and type of meterassociated with each utility account—as well as other important relevantinformation such as whether the facility is being fed by other sourcesof energy from a neighboring facility (such as steam, gas, hot water,chilled water, etc . . . ) and whether the facility itself feeds anytype of energy to other neighboring facilities (such as gas, hot water,steam, etc . . . )

The Systems Information section provides general information regardingthe structure and layout of the various heating, cooling, and electronicsystems at the facility. Also information that would indicate if this isan all-electric facility, a facility that has dual-fuel capability, etc.

The Interactive Notes section provides additional information of anykind. It may include the date of the new entry, category of the newinformation (i.e. electricity, gas, or water related), followed by thetitle and description of the new information. The name of the author aswell as the date of entry will be automatically recorded by the system.The kind of information that can be entered in this component includesinformation such as a record of a major water leak, new boilerinstallation, replacement of a faulty electric meter, a major vacancy orrenovation, and/or the like.

Turning to the Knowledge Base aspect of the Expert module, the KnowledgeBase component mainly includes providing a collection of facts,suggestions, and recommendations on how to operate a facility forimproved operating and financial efficiency, in certain exampleembodiments. The Knowledge Base may include Basic Energy Terminology,illustration of inefficient operations, energy management suggestions,and/or a checklist for energy efficient operations.

The Basic Energy Terminology section may include definitions of electricenergy, electric power, how utilities measure electric demand, as wellas the definition of Load Factor and other important parameters.

The Illustration of Inefficient Operations section builds on animportant aspect of U.S. Pat. No. 6,366,889 B1 to the same inventor byproviding illustrations, characteristics, criteria, and explanation ofdocumented patterns which could represent inefficient operations forwhich diagnosis and solutions are known (based on empirical analysisover a long period of time). This section also provides operatingengineers recommendations and guidelines on how to best operate theirfacilities in order to avoid the occurrence of similar inefficientoperations in the future.

The Energy Management Suggestions section includes providingrecommendations on improved and/or preferable methods to operatechillers, boilers, air handlers, emergency generators, and pumps underdifferent operating conditions.

The Checklist for Energy Efficient Operations section includes springand summer checklist for energy efficient operations, no cost and lowcost energy management opportunities, and potential energy conservationopportunities for office buildings and warehouses, etc.

The Action Control List(s) component may have temperature and/orconsumption-based aspects. In certain example embodiments, the ActionControl List(s) component can be used to provide a list of predefinedactions—or sequences of actions—that operating engineers would berecommended to take when a combination of specific operation and/ortemperature conditions has been met. For example, this component canprovide the sequence of equipment to turn on and how long to wait beforeinitiating the startup of each piece of equipment after an electricpower outage, in order to avoid a demand spike which would result if allequipment were to suddenly start up at the same time after a poweroutage. It could also provide the sequence of equipment to startup andhow long to delay each startup if the outside temperature falls below acertain predefined level on cold winter days in order to avoid electricdemand spikes on the coldest winter days. The list can also be used forweekend operations, for sudden temperature increases in the middle ofthe day, etc.

The virtual automatic computerized Brain Trust component of the Expertmodule may serve several distinctive purposes, in certain exampleembodiments. For example, it may leverage information collected fromsystem participants to improve and/or optimize the efficiency ofcommodity markets by listing the contract parameters of participants'commodity contracts—without divulging their “owners”, in certaininstances. This may provide other participants with “benchmarks” fromwhich to conduct their negotiations with commodity suppliers.Furthermore, through the Brain Trust, the system participants may beranked (for example) in order to see how their operating performanceand/or efficiency compares to that of other system participants.

In certain example embodiments, a comparison among different facilitiesfor energy and/or commodity usage and/or cost on a particular day mayadvantageously show system participants whether any deviations fromnormal operations and/or costs are localized and/or specific to aparticular participant, or whether the deviations from normaloperations/costs similarly affected different participants in the sameindustry, or with the same type of facility, or in a particular physicallocation. For example, abnormal weather, city-wide events, or a uniquesituation affecting one particular type of facility may be used by theBrain Trust to explain deviation patterns in system participants'operations. Additionally, the lack of similar deviation patterns mayalso be indicative of a specific problem at a specific location of asystem participant's facility.

The Brain Trust component may also leverage the use, price, and costanalytics of system participants in order to get a better understandingof how price relates to consumption and peak demand levels, correlatingconsumption with price, price with Load Factor, Load Factor with type offacility for different commodity suppliers and/or energy markets, incertain example embodiments.

The Brain Trust component of the Expert module may also provide for an“Experts' Forum”, which may serve as a platform for collaborating tofind solutions to common problems facing energy mangers, and/or as aplatform for the sharing of knowledge and ideas regarding theavailability and the implementation of new energy systems, methods, andtechnologies.

Additionally, in certain instances, the Brain Trust component may alsoprovide a “Vendors' Corner” which enables vendors to advertise newtechnologies, systems, and services and for users to be aware of thesenew systems, methods, and technologies.

The Brain Trust may even, in certain instances, provide for featurearticles, case studies, and other contributions by participating membersas well as by a professional staff of energy experts consistent withthose provided by existing energy and industry publications.

In certain example non-limiting embodiments, the entire Expert moduleand/or certain aspects of the Expert module may include artificialintelligence technology. However, it other examples, it may not.

FIG. 10 is a block diagram illustrating certain example embodiments ofthe Expert module. FIG. 10 illustrates that the above-described sectionsand components of the Expert module may all contain information that isinputted to a processor. The processor may include the Operationsmodule, in certain examples. The inputted information is analyzed by theExpert module via the processor, and the processor outputs, for example,context for the operational performance, interpretation of operationaldata, optimization of information identified and/or quantified in theoperations module, explanation of any discrepancies, suggestions forimprovement of operations performance, comparison of energy efficiencyand/or commodity usage and/or expenditures as between similar types offacilities and/or facilities in similar physical locations. Furthermore,the analysis of the Expert module may further produce temperature and/orconsumption based action suggestions. For example, if the analysis showsthat particular temperature and/or consumption-based limits have beenexceeded, suggestions for reduction in temperature and/or consumptionmay be outputted by the processor. Additionally, certain actions may betriggered by the analysis in some instances.

FIG. 11 illustrates a snapshot of an example (e.g., partial) Expertmodule. In certain example embodiments, a pull-down menu on the upperleft-hand-side of the Fig. may provide instant navigation to the variouscomponents of the Expert module.

The information presented in the FIG. 11 illustration described above isan example non-limiting embodiment, and may be arranged and expandedupon at will. Further, the illustrations of the example variouscomponents relating to certain example embodiments are being presentedin the context of a “full-fledged” performance management system asprovided by Efficiency3 Corp. Such a system typically encompasses othercomponents such as a “Dashboard” section, a “Forum” section, and a“Reports” section.

Turning to the Cost module, in certain example embodiments, the Costmodule is a component for the identification of financial waste and/ormonetization of operating waste. It also may provide substantiallyaccurate financial measurements of the results of implemented energymanagement solutions, and substantially accurate assessments of thesavings and the opportunity costs of proposed energy managementsolutions. In certain example embodiments, the Cost module may includeat least some and/or all of the following four components:

(1) Comprehensive Bill Analysis: This aspect of the cost module mayinclude a detailed line-item by line-item recompilation of monthly billsfor the current and up to two most recent fiscal years—based on theapplied utility rate structures—including detailed cash flow analysis.This component is used for the identification of billing errors(financial waste) as well as for understanding how energy operationsimpact energy costs.

(2) What-If Financial Modeling: This component allows the user to modifyany aspect of energy use and price (as laid out in the ComprehensiveBill Analysis section above) and derive the resulting difference incost. This component can be used to monetize hidden operating andfinancial waste, provide accurate assessments of the savings and theopportunity cost from proposed energy management solutions, measure theactual savings from implemented energy management solutions, and assistin budget planning.

(3) Visual Indicators: This component provides detailed visual analysisof every component of energy use, price, and cost of every utility billover for a period of up to 3-years. This component provides for thequick detection and explanation of unusual patterns in use, price, andcost.

(4) Interactive Online Access: This component provide instant access tohistorical snapshots of billing records, instant access to the appliedutility rate structures and commodity contracts, instant access to billarchives and an interactive log under the historical snapshots of thebilling records to allow users to comment on, and explain, unusualpatterns in use, price, and cost, such as billing errors, cancelledbills, partial bills, etc.. This component helps with the quickunderstanding of the applied utility rate structures, for negotiatingbetter commodity contracts, for a quick grasp of current and historicalbilling profiles, as well as for analyzing the monthly cash flow ofutility transactions.

With respect to the Comprehensive Bill Analysis, FIG. 12( a) illustratesan example method of the line-item by line-item recompilation of afacility's monthly electric bills. FIG. 12( a) represents a partial12-month representation of applied electric rates. In contrast to manycompeting software programs that use a database driven universal rateengine to re-compute a facility's utility bills, certain exampleembodiments may utilize Microsoft Excel spreadsheets configuredspecifically to mirror the intricacies of the rate schedules applied tothe subject facility.

FIG. 12( b) represents the last section of the line-item by line-itemrecompilation method explained above.

A snapshot of example “What If” financial modeling is shown in FIGS. 13(a)-(b). In the “Cash Flow” section, at the bottom of the spreadsheetillustrated, for example, in FIG. 13( b), analysts can add commentsregarding special charges, out-of-range parameters, or any other matterthat require users' attention, in certain examples. In fact, analystscan add comments into all lighter colored cells of the spreadsheet.Lighter-colored cells represent data entry cells as opposed to thedarker cells, which represent results of computations. Also, a “What If”button may be displayed on the upper right hand side of the spreadsheet.Users can click on this button to access the “What If” analyticalfunction of the spreadsheet.

What-If Financial Modeling: FIG. 14( a) illustrates a partial snapshotshowing how this component may allow a user to modify any aspect ofenergy use and/or price (as laid out in the Comprehensive Bill Analysissection above) and derive the resulting difference in cost.

By clicking on the “What If” button (displayed in the circle on theupper-right-hand corner of the snapshot), the user enters the “What IfFinancial Modeling” mode and “unlocks” the lighter shaded cells of thespreadsheet enabling these cells to be “overridden” with new numbers.The rectangle shown in FIG. 14( a), when performing comprehensive billanalysis, highlights that the difference between the computed bill (ascomputed by Comprehensive Bill Analysis spreadsheet) and the billedamount as provided by the utility company should be equal to zero.

FIG. 14( b) illustrates how by changing the “Maximum kW Use” number forthe month of March from 845 kW in FIG. 14( a) to 700 kW (the changeillustrated in FIG. 14( b)) would result in a computational discrepancyof $962. This computational discrepancy, being negative, represents areduction in cost of $962 due to the lower peak kW demand. Varying otherconsumption and price numbers would result in other correspondingchanges in costs. Furthermore, clicking on the camera button illustratedin FIG. 14( a) would result in a copy of the current spreadsheet beingtaken and open as a separate Excel file depicting a separate scenario.Still referring to FIG. 14( a), pressing the “Reset” button on the upperright hand side of the snapshot would result in resetting the What-Ifspreadsheet to its original configuration—prior to the modification ofany number. This process is used to quantify waste and savingsopportunities as well as to perform budget projections.

Visual Indicators: This component may provide a detailed visual analysisof one or more components relating to energy use, price, and/or cost ofevery utility bill over for a period of up to 3-years for the quickdetection and explanation of unusual patterns in use, price, and cost.

FIG. 15( a) illustrates a snapshot showing the first page of the VisualIndicators component. Detection and explanation is performed as follows:When an out-of-range parameter is identified in one graph for aparticular month, it is matched with corresponding visual parameters forthe same month in other graphs (of the Visual Indicators component)until an answer is found. In this page, the first three months of 2007represent the building commissioning phase.

FIG. 15( b) illustrates a snapshot showing the second page of the VisualIndicators component. The first graph shown in FIG. 15( b) illustratesthat the average consumption per day for the month of March 2007 is notnoticeably different from that of FY 2008 and FY 2009, but in the secondgraph, the kWh for FY 2007 is noticeably lower than those of FY 2008 andFY 2009. A look at the “Number of Days” table in FIG. 15( a) indicatesthe reason for the low reported kWh consumption is the result of themuch shorter billing period for that month.

FIG. 15( c) illustrates a snapshot showing the third page of the VisualIndicators component. Note in the second graph in FIG. 15( c) that thecommodity price and the overall electric price had dropped noticeably inthe month of July 2008. After looking at the line-item by line-itemanalysis of the July 2009 bill, it turned out that the utility companyhad failed to bill the facility for some of the monthly charges on thatbill—hence the unusually low commodity price. The lower commodity priceresulted in a lower Monthly Electricity Price, which in turn resulted ina lower overall dollar cost and cent/GSF figure in the graphs shown inFIG. 15( c).

As stated above, the lower commodity price resulted in a lower MonthlyElectricity Price, which in turn resulted in a lower overall dollar costand a lower cent/GSF figure, as shown in the graph of FIG. 15( d).

FIGS. 15( e)-(g) illustrate further example graphs from the visualindicators aspect of the costs module. FIGS. 15( e)-(g) do not showand/or report any unusual parameters.

Interactive Online Access: FIG. 16( a) illustrates the online access tothe Cost Module. Each building is typically represented by an actualimage of that building. FIG. 16( b) illustrates how clicking on an imageof a particular building will provide access to the historical visualrecords of that building. FIG. 16( c) illustrates a chart showing howclicking on a particular month for a selected fiscal year will bring animage of the billing records for that building up to the “clicked”particular month. Finally, FIG. 16( d) follows from FIG. 16( c) andillustrates how users can comment and discuss, interactively, theline-item by line-item billing information provided in the chart shownin FIG. 16( c).

In certain example embodiments, the Operations module is a novelapproach that provides unusual insight into the operations of afacility. In certain cases, other competing systems and methods may use15-minute or 30-minute interval data to show when and how energy (andparticularly electricity) is used at a given facility, certain examplesystems and methods disclosed herein may provide for the measurement ofhow much energy consumption has varied from one year to the next, orfrom one year to a specific base year. It depicts “motion” (e.g., thedifference between two points in time) and provides quantification ofthat motion, in certain example embodiments.

FIG. 17 illustrates a facilities operations analysis, which may beuseful for the detection of unusual operating profiles in certainexample embodiments. FIG. 17 illustrates a 12 hour period of operations;however, graphs may be provided for shorter and/or longer time periodsaccording to different example embodiments.

FIGS. 18( a)-(e) illustrate an example method of analyzing energyoperations over the period of a week. FIG. 18( a) illustrates theconventional method of analyzing energy operations; it includes a plotof electric demand (kW) over time. Time can be measured over 15-minute,30-minute, or hourly increments. FIG. 18( a) also displays thecorresponding temperatures over the same periods of time—which mostconventional systems do not include.

FIG. 18( b) is a graph illustrating certain example improved methodsrelated to certain example embodiments; which is to superimpose twoweekly graphs on top of each other and to synchronize them by the numberof the week in the calendar year rather than by calendar date. (i.e. thefirst Monday of the first week of the current year is synchronized withthe first Monday of the first week of the prior year—or a benchmarkyear—and so on . . . ) By synchronizing by number of week, Mondays willalways be synchronized by Mondays and holidays (e.g., that fall on aparticular day of the week every year) will always be synchronized withprior holidays.

In other words, FIG. 18( a) illustrates when and how the facility isbeing operated over time and identifies unusual patterns in energy useover time; however, in contrast, FIG. 18( b) (e.g., an aspect of certainexample embodiments) not only can illustrate when and how the facilityis being operated and identifies unusual patterns in energy use overtime; it also may provide certain example additional benefits in certainexample embodiments. For example, systems and methods related to theFIG. 18( b) example embodiment may advantageously identify areas ofwaste and savings opportunities, quantify the extent of energy waste andpossible energy savings, and/or measure the actual results ofimplemented energy management solutions.

In certain example embodiments, the Operations module may illustratewhen and/or how electricity (or any other form of energy or water) isused at a given facility (or metered area, etc.). Further, theOperations module may use information from the Expert module tounderstand the function, hours of operation, type of tenant operationsat the facility, and other useful information. Thus, in certain exampleembodiments, the Operations module may advantageously provide uniqueinsight that enables users to identify operating waste and savingsopportunities.

Moreover, by comparing the current 15-minute consumption (e.g., or30-minute, 1-hour, and/or any appropriate consumption interval) profileat a given facility with a prior (baseline) consumption interval (e.g.,15-minute, 30-minute, 1-hour, and/or the like) profile of the samefacility one can quantify the difference between the current operatingprofile and the prior year's operating profile. Finally, when combinedwith the “What If” financial modeling capability of the Cost Moduledescribed above, one can monetize the difference between the currentoperating profile and the prior (or baseline) operating profile.

FIGS. 18( c) and 18(e) show additional examples of the powerfulinterpretive analysis of certain example methods relating to theOperations Module.

In certain instances, referring to FIGS. 18( d) and 18(e), what isstriking about the illustrated graphs is not the shape of the curves,but rather the location of the baseline of the curves. The Expert moduleindicates that this is an office building that occupies a full block indowntown Washington, D.C. This is an old office building that shuts downat 6:00 pm every day—yet, the nighttime operating profile is at least50% of that of the peak daytime operating profile. This kind of profileindicates that major operating equipment is being left “On” all nightlong when there is no one in the building; resulting in major waste inenergy and cost.

FIG. 18( e) may provide three pieces of information in certain exampleembodiments: a) that this facility's energy usage does not appear tocorrelate to the weather at all. b) its nighttime base load is veryhigh—almost two thirds of its daytime peak. c) Its current year'soperating profile has been lowered from the prior year. Checkinginformation from the Expert module indicated that: a) this facilityincludes a large data processing center with continuous 24-houroperation, b) it is a government facility that uses steam for heating,so electricity usage is not related to outside temperatures in thewinter, and c) the result of the lowed “across-the-board” dailyconsumption is the consequence of a major re-lamping upgrade in thebuilding that resulted in lower wattage being used across the board.

The above are but a few examples of the usefulness of certain examplemethods disclosed herein. However, certain example embodiments are notlimited to the synchronization of weekly graphs alone. Additionally,while the graphs may be synchronized weekly, they may also besynchronized hourly, daily, monthly, etc.—they may be synched based onany desired time period.

Further example aspects of certain example embodiments of the Operationsmodule described herein may include the following four components:

(1) Instant Random Access to Online Synchronized Weekly Graphs ofOperations Data: Certain example methods may utilize Microsoft Excelmodules to manually synchronize streams of daily and weekly operationsdata—from any source—into useful graphs, in certain examples. The onlyrequirement is that the source data be provided in tabular format onlinefrom any “SQL” source. This tabular format can then be copied and pastedinto standardized MS Excel modules based on 15-minute, 30-minute, and/orhourly, etc., data. These modules will automatically synchronize theweekly and daily consumption data into useful graphs and records. Avisual snapshot of each Weekly (as an example time period) Excel Moduleis then taken which will display it automatically online in a mannerthat also provides instant random access to other independent Weekly (asan example time period) Excel Modules.

(2) Interactive Comments: This feature provides for an online capabilitythat enables users to discuss each e.g., Weekly, Excel Module andcomment on it interactively (similar to an interactive log). This wouldallow facility operators, managers, and other users of the system toexplain and document unusual patterns in energy consumption for futurereference. The implementation of this feature can be achieved throughthe use of common web design tools such as Adobe “Dreamweaver”.

(3) Visual and Tabular Display of the Daily Energy and Weather Data:This feature includes linking each visual snapshot of the Weekly ExcelModules into the source modules for instant download to the user'sdesktop over the Internet. Each Excel module will provide visual andtabular display of the daily energy and weather data for each week inthe calendar year in comparison to the same period of the prioryear—along with the daily difference between the current year's data andthe prior year's data, and the various percentages that thesedifferences represent. The implementation of this feature can beachieved through the use of common web design tools such as Adobe“Dreamweaver”.

(4) Instant Access to Synchronized Daily Graphs of Operations Data: Thisfeature represents another aspect of the functionality of the WeeklyExcel Module. It provides for the instant and separate visualrepresentation of each day of the Weekly Excel Module. This allows usersto “zoom in” and focus on the daily operations of each day of the weekindependently of the others.

A more detailed description of certain examples of the four examplecomponents of the Operations Module discussed above will now beprovided.

FIG. 19( a) shows an example interface of online access to theOperations Module. Each building may be represented by an actual imageof that building.

FIG. 19( b) illustrates how clicking on an image of a particularbuilding will provide access to the historical operating records of thatbuilding.

FIG. 19( c) shows how clicking on a particular week for a selectedfiscal year will bring an image of the operating records for that week.

FIG. 20 illustrates an example user interface displaying and/orpermitting interactive comments. In certain example embodiments, theInteractive Comments feature may provide for an online capability thatenables users to discuss each Weekly Excel Module and comment on itinteractively (similar to an interactive log). This would allow facilityoperators, managers, and other users of the system to explain anddocument unusual patterns in energy consumption for future reference.

FIG. 21( a) illustrates an example embodiment of a visual and tabulardisplay of daily energy and weather data. In certain exampleembodiments, the visual and tabular display of the daily energy andweather data feature may include linking each visual snapshot of theExcel Weekly Modules into the source modules for instant download to theuser's desktop over the Internet (by clicking on the shaded area shownin FIG. 21( a)). Each Excel module will provide visual and tabulardisplay of the daily energy and weather data for each week in thecalendar year in comparison to the same period of the prior year—alongwith the daily difference between the current year's data and the prioryear's data, and the various percentages that these differencesrepresent.

FIG. 21( b) illustrates a snapshot of the front page of the sourcemodule of an Excel Weekly Module. As FIG. 21( b) shows, the sourcemodule provides visual and tabular display of the daily energy andweather data for each week in the calendar year in comparison to thesame period of the prior year—along with the daily difference betweenthe current year's data and the prior year's data, and the variouspercentages that these differences represent.

FIG. 21( c) shows an example snapshot of the second page of the sourcemodule of an Excel Weekly Module. As FIG. 21( c) illustrates, this partof the example Excel Weekly Module provides the numerical values of thegraphs provided in snapshot of the front page of the Excel Weekly Module(e.g., as shown in FIG. 21( b)).

FIGS. 22( a)-(b) illustrate examples of the “Instant Access toSynchronized Daily Graphs of Operations Data,” another example aspect ofthe functionality of the Weekly Excel Module. It provides for theinstant and separate VISUAL representation of each day of the WeeklyExcel Module in certain example embodiments. This may permit users to“zoom in” and focus on the daily operations of each day of the weekindependently of the others.

While the design of certain example embodiments relating to the methodsand systems disclosed herein may rely on access to electric “intervalpulse data” from the local utility company, which typically providessuch data electronically online from a few days to a few weeks inarrears, certain example methods may be adapted to use any stream ofinterval data from any source as long as it can be displayed ordownloaded electronically in tabular format. If fact, the source of theinterval pulse data can be from any utility system or sub-meteringsystem that can provide electricity and/or gas data, on a 15-minute,30-minute, or hourly intervals—in “real-time” or through “delayed”(e.g., retroactive) delivery.

FIGS. 23( a)-(b) illustrate screen shots from an example “delayed” datasystem/retroactive analysis, and an example “real-time” system,respectively. More specifically, FIG. 23( a) illustrates a possible“screen shot” of the method of the system described above using delayeddata streams. This approach is best for analyzing the operatingperformance of a facility from an overall (macro) performance managementpoint of view. FIG. 23( b), on the other hand, illustrates a possible“screen shot” from the method of the system described above using “live”or real-time data streams. This approach may be operationally (micro)advantageous in certain examples.

However, unfortunately, certain existing systems may only providelimited data available in a real-time stream. An example of such anexisting system is shown in FIG. 24. Accordingly, it will be appreciatedthat it may be desirable to combine certain example methods with examplesystems that can provide electricity or gas data in 15-minute,30-minute, or hourly interval data—in real time. In certain exampleembodiments, the implementation of certain example methods and systemsfor live data streams described herein may be advantageous, and mayrepresent an improvement over existing real-time/live data streams; ascan be seen, for example, from FIG. 23( b). For example, such systemsand methods may advantageously provide a great value from an operationalstand point.

FIG. 25 provides a non-limiting improved system overview andimplementation methods for “live” or “real-time” data streams. Forexample, FIG. 25 illustrates that certain example live data streamembodiments may include an Expert module, a Costs module, an Operationsmodule, and Action control lists.

Certain example embodiments of systems and/or methods relating toimproved “real-time” or “live” data streams will now be described. Incertain example embodiments, for example as shown in FIG. 25, theimplementation of certain example methods for real-time data streamswill have four—instead of three—modules. The modules may include, forexample, an Expert module, a Costs module, an Operations module, and mayadditionally include an Action Control Lists module; as illustrated inFIG. 25. Each of these modules will be described in more detail below.

(1) Expert Module: This module may include the same components describedabove except for the “Action Control List” component. This component nowbecomes a separate module in certain instances. In certain exampleembodiments, the Expert module may be similar to that described inconnection with the delayed-delivery system above.

(2) Costs Module: For live data streams, this module will include“What-If” Financial Modeling Templates, in certain examples. “What-If”Financial Modeling Templates includes various templates which can beshaped to closely mimic applied, available, or envisioned electricityand/or gas rate structures. These templates can access the energy usedata generated by the “real-time” system, multiply that consumption by auser-defined average “distribution” rate, a user-defined average“commodity” rate, plus any other factor which might be necessary, suchas a “kW Demand” rate and an overall sales tax rate to derive anapproximate cost for the energy used. Different rate templates can beshaped, stored in the system, and applied to the energy data collectedby the system.

For example, FIG. 26( a) illustrates an example “Costs” template thatmay be used to provide an ongoing data stream of cost data which would“monetize” the cost of the displayed consumption as well as the savings,or additional costs, incurred from the difference in energy use betweenthe live data stream and the chosen base year.

FIG. 26( a), for illustration purposes only, shows tabular data that islimited to 12 hours only (half a day). For an actual system, in certainexample embodiments, most display screens are large enough to easilyaccommodate data that encompass 24 hours of data (for a full day ofanalysis).

FIG. 26( b) illustrates an example schematic of a possible Costs Module“What-If” Template. In certain instances, by adding a financial “WhatIf” module, one can monetize the quantity of energy that is varying fromone year to the next.

Using the “What-If” financial modeling template illustrated in FIG. 26(b), a “Real-Time” system can compile the energy use and Peak Demand dataautomatically from the metering stream (based on a 15-minute, 30-minute,or any other parameter). In certain example embodiments, the users wouldonly have to enter the energy prices for Distribution and Commodity, anaverage peak demand price when applicable, and any additional taxes andsurcharges. In certain instances, the system may then be able to computethe resulting costs for the desired time period automatically.

Another “What-If” financial modeling template could be designed to breakdown the energy prices by time of day (e.g., On-Peak, Off-Peak, etc.),in certain examples. Yet another template could use available“Real-Time” pricing options to determine the price of the sameconsumption patterns under different pricing options, in other exampleembodiments. It will be appreciated that certain “What-If” templatesdescribed herein may be used to determine many different cost-relatedissues.

FIG. 26( c) illustrates that the amount of energy used, times the price,is the total energy cost. Furthermore, the actual amount used, times theactual price, will equal the actual computed cost. The difference incost between the actual cost and the billed amount should always equalzero. This modeling can be used to determine the cost of an alternativequantity of energy. for example, the alternative quantity may bemultiplied by the actual price, to arrive at the alternative computedcost. In certain instances, the cost difference between the alternativecost and the billed amount will give the cost differential of thealternative quantity, in certain example embodiments.

(3) Operations Module: In certain example embodiments, the concept ofsynchronizing current energy consumption with the corresponding periodof the prior year, or a pre-selected base year, as well, is done bycertain example methods of synchronization used by the OperationsModule. For example, the Operations module may include instant access tosynchronized daily and/or weekly operations graphs with tabular displaysof energy and weather data. Furthermore, in certain example embodiments,the Operations module may permit instant access to synchronized graphsof operations data with a tabular display of energy and weather data,over any period of time. Finally, the Operations module may also includeinteractive comments.

(4) Action Control Lists: Another example advantage of certain examplemethods and systems, when applied to “real-time” systems, is to initiatea sequence of events automatically whenever certain combinations ofoperating conditions and temperatures have been met.

In certain instances, incorporating certain example methods disclosedherein with “live” or “real-time” data streams could enhance the valueof certain example embodiments exponentially. The following is a list ofnon-limiting example features associated with certain “real-time”aspects of certain example embodiments.

-   -   It could add a frame of reference to the data streams.    -   It could provide “context to the content” of the data streams.    -   It could enable instant commenting on unusual energy profiles.    -   It could provide for instant referral and execution of select        “Action Scripts” when certain operating and environmental        conditions have been encountered.    -   It could provide “valuation” and meaning to the energy        monitoring process.    -   It could provide an automatic visual and audible alarm—not only        when a certain peak consumption (electric demand) condition has        been reached—but also when energy consumption has increased        beyond a specific, pre-defined, level of energy use from the        prior year—or from a pre-selected “benchmark profile”.    -   It could even provide for the automatic implementation of        “Action Control” scripts that would automatically shut down,        startup, or control certain equipment when specific criteria        have been met.

The value of the concept is self-evident; in certain instances itprovides a frame of reference against which to compare current energyconsumption. In addition, without a frame of reference, one cannotmeasure (quantify) or monetize the performance difference from one yearto another, in certain instances. Certain example methods ofsynchronization described herein are very simple; includingsynchronizing each weekday of the year with a corresponding weekday fromthe prior year. By that we mean that a Monday should be synchronizedwith a Monday, a Tuesday, with a Tuesday, and so on.

For example, in certain example embodiments, there may be a need for anExpert system to identify, understand and explain usual and unusualoperating profiles, in real-time. Certain example methods and systems ofsynchronization described herein stipulate that in order to achieve suchsynchronization, the difference between the two days shall be exactly364 days. It may be advantageous, for example, to have the ability tomeasure and quantify energy consumption changes (increases/decreases)from the prior year by synchronizing back exactly 364 days (or multiplesof that number for going back to a base year up to 4 years inarrears—anything more ancient than that would be meaningless).Synchronizing with 363 or 365 days will result in Mondays beingsynchronized with Sundays and Fridays being synchronized with Saturdays.Also, synchronizing one month, three months, or six months in arrearswill be meaningless as it would straddle seasons where operations aredrastically different. This leave the 364 days (exactly 52 weeks) as anexample meaningful synchronization method, in certain exampleembodiments. In other examples, 350, 357, 371, and/or 378 days may alsobe close enough so as to result in meaningful synchronization.Correlation can be displayed at the level of 1 day, one week, one month,or one year, and anything in between, according to different exampleembodiments.

For example, Saturday, Dec. 30, 2011 shall be synchronized withSaturday, Dec. 31, 2010, and Saturday Dec. 31, 2010 shall besynchronized with Saturday, Jan. 1, 2010, and Saturday Jan. 1, 2010shall be synchronized with Saturday, Jan. 2, 2009 and so on. The exactdifference between each of these days is exactly 364 days regardlesswhether year in question is a leap year or a non-leap year.Synchronizing with a period of less or more than 364 days would bemeaningless because of the change in seasonal temperatures. For example,synchronizing over a single month, a three-month, or a six-month periodwould be meaningless because typically there is a vast difference inoperating profile from October 15th to November 15th (One monthdifference), from September 31st to December 31st (three-monthdifference), and from February 15th to August 15th (six-monthdifference).

This formula works well from any given date to a date exactly 364 daysin arrears. It also works well for up to a four-year period in arrears.For example, if a user wishes to compare the current operating profileof a facility to the same period four years in arrears (i.e. a baselinethat is four years in arrears), all that person has to do is select adate that is (4 years×364 days in arrears), for a period that is 3 yearsin arrears, the formula would be (3 years×364 days in arrears).Synchronizing weekdays over periods in excess of four years begins tointroduce a noticeable difference in calendar days (i.e. comparingSunday, Jan. 1, 2000 to a Sunday five years in arrears would result inthe selection of Jan. 7, 1995. Such calendar discrepancies can beavoided by deducting the number (7) from the formula after the firstfour years (i.e. CURRENT DATE—(364*5)−7), but one can hardly see areason to compare current consumption to a period 5 years in arrears, orearlier.

The chart illustrated in FIG. 27 shows how synchronizing to a periodexactly 364 days in arrears in a given year, in a non-limiting exemplaryexample embodiment, will result in a match for the same weekday from oneyear to another.

In other words, FIG. 27 shows the results of daily synchronization witha prior year; weekly synchronization can also be easily achieved byselecting a period 364 days in arrears for the beginning of the week,and adding 6 days to that number to derive the ending period of theweek. It is noted that the synchronization described with respect toFIG. 27 and other example embodiments may be applied to both and/or oneof the “delayed delivery” and the “real-time” systems described herein.

In addition to providing the ability to quantify and monetize real-timeenergy performance with a corresponding period from the prior year,certain example advantages of this method may include providing a moremeaningful performance management system by providing a visual signal(and perhaps an audible signal as well) whenever a certain combinationof energy consumption and temperatures have varied by a pre-determinedlevel from the chosen synchronized “baseline”—at any time of the day ornight. For example, if the outside temperature over the course of agiven hour is within 5% of the prior year but energy consumption hasrisen 20% or more, a specific visual indicator may turn a particularcolor (e.g., red). When consumption and temperatures are within anacceptable range, the color may change (e.g., to green), and whenconsumption is rising, but has not reached a specific threshold, thecolor could change to a different color from the previous two colors(e.g., orange). In certain instances, it may be desirable to providewarnings and/or indications indicative of the current consumption level,rather than only providing a warning when energy consumption or the peakelectric demand has exceeded a certain threshold.

In addition to providing visual and audible warnings, in certain exampleembodiments, the system may be made to initiate or suggest the executionof specific “Action Control Scripts” and/or “Action Control Lists” basedon predetermined parameters of energy use, weather, or any otherparameters that are being tracked (e.g., vacancy rates, number of mealserved, etc.).

With respect to Action Control Lists, FIG. 28 illustrates how awarning/alarm system could be implemented as part of the describedmethods and systems. More specifically, as mentioned above, anotheradvantage of these methods and systems when applied to “real-time”systems is to initiate a sequence of events automatically whenevercertain combinations of operating conditions and temperatures have beenmet. In fact, most modern building energy management systems contain afeature that turns equipment on automatically at a predetermined hour inthe morning and shuts them down automatically at the end of the workingday. What is unique to this method, it that instead of the actioncontrol scripts being tied to a specific time or a specific level ofpeak electric demand, they can be triggered by a combination of eventssuch as difference in outside temperatures being similar to the prioryear yet energy consumption is continuing to creep up substantially.Under such circumstances, in addition to providing visual and audiblewarnings, non-limiting examples could include sending an email or avoice recording to the building manager, or signals could automaticallybe sent to the facility's chillers or boilers to initiate a modificationof their temperatures to predetermined levels. All sorts of scenarioscould be envisioned for the use of Action Control Lists tied to acombination of operating factors and temperatures.

In other words, the method and system of this patent application can beused on “real-time” data streams or “delayed-delivery” data streams. Theapplication of this method on real-time data streams will vastly enhancethe ability of operating engineers to maximize the operating efficiencyof their facilities. On the other hand, the application of the method ofthis patent application on delayed-delivery data streams would vastlyenhance the ability of energy managers and facility managers tointerpret and optimize usual and unusual patterns in energy consumption;identify, quantify, and monetize hidden operating and financial waste;and accurately measure the results of implemented energy managementsolutions—in a reduced amount of time with decreased cost and effort.

While the invention has been described in connection with what ispresently considered to be the most practical and preferred embodiment,it is to be understood that the invention is not to be limited to thedisclosed embodiment, but on the contrary, is intended to cover variousmodifications and equivalent arrangements included within the spirit andscope of the appended claims.

1. A method of synchronizing current and/or recent operating pattern(s)with corresponding operating pattern(s) of a prior year, the methodcomprising: storing current and/or recent operating patterns on at leastone storage device, the current and/or recent operating patternscomprising current and/or recent incremental commodity usage data;storing operating patterns of at least a year prior to the currentand/or recent operating patterns on the at least one storage device, theprior year operating patterns comprising historical incrementalcommodity usage data; synchronizing, via a processor coupled to thestorage device, the current and/or recent operating patterns withoperating patterns of at least a year prior, based at least in part onperiodically-repeating time periods over which the usage occurred togenerate an incremental historical comparison of usage from differentbut related periodically-repeating time, that at least partly takes intoaccount time-variable factors affecting the usage, including using theprocessor to automatically correlate usage data that is exactly aninteger multiple of 364 days apart.
 2. The method of synchronization ofclaim 1, further comprising outputting a graphical visualization of theincremental historical comparison to a display coupled to the processor.3. The method of synchronization of claim 1, wherein the given day inthe current year is synchronized to a same day of the week in the atleast a prior year.
 4. The method of synchronization of claim 1, whereinthe periodically-repeating time period ranges from about 15 minutes toone year.
 5. The method of synchronization of claim 1, furthercomprising identifying usual and/or unusual energy consumption patternsfrom a comparison between the respective operating patterns.
 6. Themethod of synchronization of claim 5, further comprising accuratelymeasuring and quantifying any difference in energy consumption asbetween the respective operating patterns.
 7. The method ofsynchronization of claim 5, further comprising: receiving comments froma user that can reference, document, and explain the usual and/orunusual operating parameters displayed in an operations module;associating the received comments with the usual and/or unusualoperating parameters in an interactive log.
 8. The method ofsynchronization of claim 1, further comprising outputting a real-timedata stream to the display, the real-time data stream includinginformation on consumption, temperature, and cost data.
 9. The method ofsynchronization of claim 1, further comprising outputting to the displayadditional information that includes interpretation and explanation ofthe displayed operating patterns.
 10. The method of synchronization ofclaim 1, further comprising monetizing a difference in energyconsumption as between the respective operating patterns.
 11. The methodof synchronization of claim 1, further comprising: determining whencertain combinations of operating conditions and temperature differenceshave been met; outputting, based on the determination, automatednotifications and/or visual and/or audible warning signals.
 12. Themethod of synchronization of claim 11, further comprising: triggering,via the at least one processor, pre-determined Action Control Scriptswhenever certain combinations of operating conditions and temperaturedifferences have been met.
 13. A system for monitoring and reportingusage of a commodity such as energy comprising: at least one storagedevice configured to store historical incremental commodity usage data;and a processor coupled to the storage device, the processortime-correlating the historical incremental usage data based at least inpart on periodically-repeating time periods over which the usageoccurred to generate an incremental historical comparison of usage fromdifferent but related periodically-repeating time periods that at leastpartly takes into account time-variable factors affecting the usage, theprocessor automatically identifying unusual usage patterns by comparingwith synchronized historic usage patterns and automatically providingcontextual interactivity for the usage patterns based on a storedknowledge base, temperature- and/or consumption-based action controllists, and information including commodity pricing, and/or facilitiesanalytics.
 14. The system of claim 13, wherein the processor furtherautomatically computes differences in commodity usage as betweendifferent years on an at least daily granularity.
 15. A system fortracking performance of a facility relating to energy and/or waterusage, the system comprising: at least one storage device configured tostore historical and/or current incremental commodity usage and/or costdata; a first computer-implemented module comprising a plurality ofcomponents, the components comprising a facility information component,a cost analysis component, and an operations analysis component; thefacility information component comprising data pertaining to aparticular facility, comprising a description of the facility, afunction of the facility, operating hours of the facility, and/orinformation relating to usage of utilities by the facility, stored onthe storage device; the cost analysis component comprising historicalbilling data stored on the storage device; the operations analysiscomponent comprising data corresponding to a plurality of weekly modulescomprising 15 to 30 minute interval usage and weather data for each dayof the week; and a second computer-implemented module comprisingperiodic updates to the cost analysis and operations analysiscomponents; a processor configured to execute the first and secondcomputer-implemented modules; and wherein the data corresponding to theweekly modules from a prior year and from a current year aresynchronized by day of week such that data from a Monday of a particularweek in the prior year and data from a Monday of a corresponding week inthe current year are synchronized in order to accurately trackperformance of the facility with respect to energy and/or water usage.16. The system of claim 15, wherein the facility information componentfurther comprises at least a description of the structure and/or layoutof heating, cooling, and/or electronic systems of the facility stored onthe storage device.
 17. The system of claim 16, wherein the facilitycomponent further comprises operational guidelines comprising at leastillustrations, characteristics, criteria, and/or an explanation ofdocumented patterns that may represent inefficient operations stored onthe storage device.
 18. The system of claim 17, wherein the operationalguidelines of the facility component additionally comprise a diagnosisof and/or solutions to at least some of the documented patters that mayrepresent inefficient operations stored on the storage device.
 19. Thesystem of claim 15, wherein the facility component further comprises alist of predefined actions and/or sequences of actions that may beimplemented when a combination of specific operation and/or temperatureconditions have been met stored on the storage device.
 20. The system ofclaim 15, wherein the operations analysis component comprises presentand/or historical weather data stored on the storage device.
 21. Thesystem of claim 15, wherein the facility component further comprises aninteractive, online log comprising entries of issues such that a usermay post, view, and/or comment on each entry individually.
 22. A methodof tracking performance trends relating to utility usage, the methodcomprising: synchronizing 15 to 30 minute intervals of data from aparticular week in first year with 15 to 30 minute intervals of datafrom a corresponding week in a second year such that the data issynchronized by day of the week in order to track the performance ofutility usage from the particular week of the first year to thecorresponding week of the second year by comparing usage from the weekof the first year and the week of the second year in 15 to 30 minuteintervals.
 23. A non-transitory storage medium arrangement for in usebeing operatively coupled to a computing device, said computing devicein use accessing the storage medium arrangement to generate an outputincluding at least user display data, the storage medium arrangementstoring at least instructions executable by computing device tocorrelate data stored on the storage medium arrangement and generatesaid user display data output, the storage medium arrangement havingstored thereon: utility usage data defining at least utility usageincluding an amount of usage and time periods corresponding to saidusage; and instructions executable by said computing device to processusage amount at least in part based on said time periods to correlateand register said usage data accordingly to utility demand patterns thatare likely to recur in time, to thereby enable comparison betweentime-comparable demand cycle patterns.
 24. The storage mediumarrangement of claim 23 wherein said time-comparable demand cyclepatterns include weather-based utility demand patterns.
 25. The storagemedium arrangement of claim 23 wherein said time-comparable demand cyclepatterns include calendar-based utility demand patterns.
 26. The storagemedium arrangement of claim 23 wherein said time-comparable demand cyclepatterns include utility demand patterns based on day of the week andweeks of the year, and said correlating includes correlating byregistering usage data for a second day with a first day by subtracting364 days from the second day to determine the identity of the first daywith which the second day is to be correlated, wherein the usage data ofthe first day is used as a baseline to which the usage data of thesecond day can be compared.
 27. The storage medium arrangement of claim23 wherein said time-comparable demand cycle patterns include utilitydemand patterns based on day of the week and weeks of the year, and saidcorrelating includes correlating by registering usage data correspondingto days of the week in corresponding weeks of the year.
 28. The storagemedium arrangement as in claim 23 wherein said storage mediumarrangement further stores additional instructions that update saidstored utility usage data with more current usage data.
 29. A system forprocessing historical utility usage and cost data collected from autility, said system including: at least one storage medium that storessaid utility usage data; at least one user input/output device; at leastone processor operatively connected to the storage medium and the userinput/output device, the at least one processor automatically creating abaseline based on at least said stored utility usage and cost data, saidat least one processor further updating the stored utility usage andcost data with more current usage and cost data and synchronizing theupdated data with previously stored data to generate graphicalvisualizations that allow discovery of performance discrepancies and/orusage or cost aberrations compared to said baseline; and a displaydevice operatively coupled to the at least one processor, the displaydevice displaying said generated graphical visualizations.
 30. Thesystem of claim 29 wherein said at least one processor further actsthrough the input/output device enabling a user to interactively logcauses for said discrepancies and/or aberrations.
 31. The system ofclaim 29 wherein said at least one processor further acts through theinput/out device enabling a user to use an interactive log in order tosearch for solutions to similar discrepancies and/or aberrations.
 32. Asystem for tracking performance of a facility relating to energy and/orwater usage, the system comprising: at least one storage deviceconfigured to store historical and/or current incremental commodityusage and/or cost data; a first computer-implemented module comprising aplurality of components, the components comprising a facility and/orgeneral information component, a cost analysis component, and anoperations analysis component; the facility information componentcomprising at least data pertaining to a particular facility, comprisinga description of the facility, a function of the facility, operatinghours of the facility, and/or information relating to usage of utilitiesby the facility, stored on the storage device; the cost analysiscomponent comprising historical billing data stored on the storagedevice; the operations analysis component comprising data correspondingto a plurality of modules comprising 15 to 30 minute interval usage andweather data for a given day; and a second computer-implemented modulecomprising periodic updates to the cost analysis and operations analysiscomponents; a processor configured to execute the first and secondcomputer-implemented modules; and wherein the data corresponding to themodules from a prior year and from a current year are synchronized byday of week in order to accurately track performance of the facilitywith respect to energy and/or water usage.